Overview

Brought to you by YData

Dataset statistics

Number of variables12
Number of observations782
Missing cells314
Missing cells (%)3.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory73.4 KiB
Average record size in memory96.2 B

Variable types

Text1
Categorical1
Numeric10

Alerts

family is highly overall correlated with gdp_pc and 3 other fieldsHigh correlation
freedom is highly overall correlated with rank and 1 other fieldsHigh correlation
gdp_pc is highly overall correlated with family and 3 other fieldsHigh correlation
health is highly overall correlated with family and 3 other fieldsHigh correlation
rank is highly overall correlated with family and 4 other fieldsHigh correlation
score is highly overall correlated with family and 4 other fieldsHigh correlation
dystopia has 312 (39.9%) missing valuesMissing
rank is uniformly distributedUniform

Reproduction

Analysis started2024-07-31 02:09:49.879682
Analysis finished2024-07-31 02:10:09.027520
Duration19.15 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

Distinct164
Distinct (%)21.0%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
2024-07-30T21:10:09.250520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length22
Mean length8.1649616
Min length4

Characters and Unicode

Total characters6385
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.5%

Sample

1st rowAfghanistan
2nd rowAfghanistan
3rd rowAfghanistan
4th rowAfghanistan
5th rowAfghanistan
ValueCountFrequency (%)
united 15
 
1.6%
republic 14
 
1.5%
south 14
 
1.5%
congo 10
 
1.1%
cyprus 10
 
1.1%
and 10
 
1.1%
sudan 8
 
0.9%
bangladesh 5
 
0.5%
bahrain 5
 
0.5%
dominican 5
 
0.5%
Other values (174) 826
89.6%
2024-07-30T21:10:09.785594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1002
15.7%
i 564
 
8.8%
n 514
 
8.1%
e 412
 
6.5%
r 374
 
5.9%
o 364
 
5.7%
l 235
 
3.7%
t 234
 
3.7%
u 219
 
3.4%
s 196
 
3.1%
Other values (43) 2271
35.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6385
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1002
15.7%
i 564
 
8.8%
n 514
 
8.1%
e 412
 
6.5%
r 374
 
5.9%
o 364
 
5.7%
l 235
 
3.7%
t 234
 
3.7%
u 219
 
3.4%
s 196
 
3.1%
Other values (43) 2271
35.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6385
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1002
15.7%
i 564
 
8.8%
n 514
 
8.1%
e 412
 
6.5%
r 374
 
5.9%
o 364
 
5.7%
l 235
 
3.7%
t 234
 
3.7%
u 219
 
3.4%
s 196
 
3.1%
Other values (43) 2271
35.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6385
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1002
15.7%
i 564
 
8.8%
n 514
 
8.1%
e 412
 
6.5%
r 374
 
5.9%
o 364
 
5.7%
l 235
 
3.7%
t 234
 
3.7%
u 219
 
3.4%
s 196
 
3.1%
Other values (43) 2271
35.6%

region
Categorical

Distinct10
Distinct (%)1.3%
Missing1
Missing (%)0.1%
Memory size6.2 KiB
Sub-Saharan Africa
195 
Central and Eastern Europe
145 
Latin America and Caribbean
111 
Western Europe
105 
Middle East and Northern Africa
96 
Other values (5)
129 

Length

Max length31
Median length26
Mean length21.339309
Min length12

Characters and Unicode

Total characters16666
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSouthern Asia
2nd rowSouthern Asia
3rd rowSouthern Asia
4th rowSouthern Asia
5th rowSouthern Asia

Common Values

ValueCountFrequency (%)
Sub-Saharan Africa 195
24.9%
Central and Eastern Europe 145
18.5%
Latin America and Caribbean 111
14.2%
Western Europe 105
13.4%
Middle East and Northern Africa 96
12.3%
Southeastern Asia 44
 
5.6%
Southern Asia 35
 
4.5%
Eastern Asia 30
 
3.8%
Australia and New Zealand 10
 
1.3%
North America 10
 
1.3%
(Missing) 1
 
0.1%

Length

2024-07-30T21:10:10.011585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-30T21:10:10.226633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
and 362
15.2%
africa 291
12.2%
europe 250
10.5%
sub-saharan 195
 
8.2%
eastern 175
 
7.3%
central 145
 
6.1%
america 121
 
5.1%
latin 111
 
4.7%
caribbean 111
 
4.7%
asia 109
 
4.6%
Other values (10) 512
21.5%

Most occurring characters

ValueCountFrequency (%)
a 2301
13.8%
r 1684
 
10.1%
1601
 
9.6%
n 1389
 
8.3%
e 1347
 
8.1%
t 871
 
5.2%
i 849
 
5.1%
d 564
 
3.4%
s 539
 
3.2%
u 534
 
3.2%
Other values (19) 4987
29.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16666
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2301
13.8%
r 1684
 
10.1%
1601
 
9.6%
n 1389
 
8.3%
e 1347
 
8.1%
t 871
 
5.2%
i 849
 
5.1%
d 564
 
3.4%
s 539
 
3.2%
u 534
 
3.2%
Other values (19) 4987
29.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16666
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2301
13.8%
r 1684
 
10.1%
1601
 
9.6%
n 1389
 
8.3%
e 1347
 
8.1%
t 871
 
5.2%
i 849
 
5.1%
d 564
 
3.4%
s 539
 
3.2%
u 534
 
3.2%
Other values (19) 4987
29.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16666
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2301
13.8%
r 1684
 
10.1%
1601
 
9.6%
n 1389
 
8.3%
e 1347
 
8.1%
t 871
 
5.2%
i 849
 
5.1%
d 564
 
3.4%
s 539
 
3.2%
u 534
 
3.2%
Other values (19) 4987
29.9%

year
Real number (ℝ)

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2016.9936
Minimum2015
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2024-07-30T21:10:10.448557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2015
5-th percentile2015
Q12016
median2017
Q32018
95-th percentile2019
Maximum2019
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4173644
Coefficient of variation (CV)0.00070271142
Kurtosis-1.3052698
Mean2016.9936
Median Absolute Deviation (MAD)1
Skewness0.0059038944
Sum1577289
Variance2.0089219
MonotonicityNot monotonic
2024-07-30T21:10:10.634462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
2015 158
20.2%
2016 157
20.1%
2018 156
19.9%
2019 156
19.9%
2017 155
19.8%
ValueCountFrequency (%)
2015 158
20.2%
2016 157
20.1%
2017 155
19.8%
2018 156
19.9%
2019 156
19.9%
ValueCountFrequency (%)
2019 156
19.9%
2018 156
19.9%
2017 155
19.8%
2016 157
20.1%
2015 158
20.2%

rank
Real number (ℝ)

HIGH CORRELATION  UNIFORM 

Distinct158
Distinct (%)20.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.69821
Minimum1
Maximum158
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2024-07-30T21:10:10.853400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8.05
Q140
median79
Q3118
95-th percentile149
Maximum158
Range157
Interquartile range (IQR)78

Descriptive statistics

Standard deviation45.182384
Coefficient of variation (CV)0.57412214
Kurtosis-1.1997011
Mean78.69821
Median Absolute Deviation (MAD)39
Skewness0.00049735146
Sum61542
Variance2041.4479
MonotonicityNot monotonic
2024-07-30T21:10:11.089354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34 6
 
0.8%
145 6
 
0.8%
82 6
 
0.8%
57 6
 
0.8%
153 5
 
0.6%
23 5
 
0.6%
14 5
 
0.6%
74 5
 
0.6%
77 5
 
0.6%
75 5
 
0.6%
Other values (148) 728
93.1%
ValueCountFrequency (%)
1 5
0.6%
2 5
0.6%
3 5
0.6%
4 5
0.6%
5 5
0.6%
6 5
0.6%
7 5
0.6%
8 5
0.6%
9 5
0.6%
10 5
0.6%
ValueCountFrequency (%)
158 1
 
0.1%
157 2
 
0.3%
156 4
0.5%
155 5
0.6%
154 5
0.6%
153 5
0.6%
152 5
0.6%
151 5
0.6%
150 5
0.6%
149 5
0.6%

score
Real number (ℝ)

HIGH CORRELATION 

Distinct716
Distinct (%)91.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3790179
Minimum2.6930001
Maximum7.769
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2024-07-30T21:10:11.339552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.6930001
5-th percentile3.58715
Q14.50975
median5.322
Q36.1895
95-th percentile7.31395
Maximum7.769
Range5.0759999
Interquartile range (IQR)1.67975

Descriptive statistics

Standard deviation1.1274565
Coefficient of variation (CV)0.20960266
Kurtosis-0.76105459
Mean5.3790179
Median Absolute Deviation (MAD)0.846
Skewness0.035859433
Sum4206.392
Variance1.2711581
MonotonicityNot monotonic
2024-07-30T21:10:11.582980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.379 3
 
0.4%
5.835 3
 
0.4%
5.89 3
 
0.4%
5.129 3
 
0.4%
2.905 3
 
0.4%
4.35 3
 
0.4%
5.192 3
 
0.4%
6.375 3
 
0.4%
4.681 2
 
0.3%
5.743 2
 
0.3%
Other values (706) 754
96.4%
ValueCountFrequency (%)
2.693000078 1
 
0.1%
2.839 1
 
0.1%
2.853 1
 
0.1%
2.904999971 1
 
0.1%
2.905 3
0.4%
3.006 1
 
0.1%
3.069 1
 
0.1%
3.083 2
0.3%
3.203 1
 
0.1%
3.231 1
 
0.1%
ValueCountFrequency (%)
7.769 1
0.1%
7.632 1
0.1%
7.6 1
0.1%
7.594 1
0.1%
7.587 1
0.1%
7.561 1
0.1%
7.555 1
0.1%
7.554 1
0.1%
7.537000179 1
0.1%
7.527 1
0.1%

gdp_pc
Real number (ℝ)

HIGH CORRELATION 

Distinct742
Distinct (%)94.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.91604748
Minimum0
Maximum2.096
Zeros5
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2024-07-30T21:10:11.813437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.208797
Q10.6065
median0.98220471
Q31.2361871
95-th percentile1.4878821
Maximum2.096
Range2.096
Interquartile range (IQR)0.62968711

Descriptive statistics

Standard deviation0.40734013
Coefficient of variation (CV)0.44467142
Kurtosis-0.69275951
Mean0.91604748
Median Absolute Deviation (MAD)0.29988905
Skewness-0.31858051
Sum716.34913
Variance0.16592598
MonotonicityNot monotonic
2024-07-30T21:10:12.042747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5
 
0.6%
0.96 4
 
0.5%
1.34 3
 
0.4%
0.332 3
 
0.4%
1.043 2
 
0.3%
0.642 2
 
0.3%
1.301 2
 
0.3%
0.689 2
 
0.3%
1.221 2
 
0.3%
1.263 2
 
0.3%
Other values (732) 755
96.5%
ValueCountFrequency (%)
0 5
0.6%
0.0153 1
 
0.1%
0.01604 1
 
0.1%
0.02264318429 1
 
0.1%
0.024 1
 
0.1%
0.026 1
 
0.1%
0.046 1
 
0.1%
0.05661 1
 
0.1%
0.06831 1
 
0.1%
0.069 1
 
0.1%
ValueCountFrequency (%)
2.096 1
0.1%
1.870765686 1
0.1%
1.82427 1
0.1%
1.741943598 1
0.1%
1.69752 1
0.1%
1.69227767 1
0.1%
1.69042 1
0.1%
1.684 1
0.1%
1.649 1
0.1%
1.64555 1
0.1%

family
Real number (ℝ)

HIGH CORRELATION 

Distinct732
Distinct (%)93.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0783925
Minimum0
Maximum1.644
Zeros5
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2024-07-30T21:10:12.264008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.46133
Q10.8693625
median1.124735
Q31.32725
95-th percentile1.522
Maximum1.644
Range1.644
Interquartile range (IQR)0.4578875

Descriptive statistics

Standard deviation0.32954832
Coefficient of variation (CV)0.30559219
Kurtosis0.15844868
Mean1.0783925
Median Absolute Deviation (MAD)0.235555
Skewness-0.68463229
Sum843.30292
Variance0.10860209
MonotonicityNot monotonic
2024-07-30T21:10:12.494445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5
 
0.6%
1.538 3
 
0.4%
1.438 3
 
0.4%
1.465 3
 
0.4%
1.125 3
 
0.4%
1.41 3
 
0.4%
1.504 3
 
0.4%
1.319 2
 
0.3%
1.223 2
 
0.3%
1.459 2
 
0.3%
Other values (722) 753
96.3%
ValueCountFrequency (%)
0 5
0.6%
0.10419 1
 
0.1%
0.11037 1
 
0.1%
0.13995 1
 
0.1%
0.147 1
 
0.1%
0.14866 1
 
0.1%
0.18519 1
 
0.1%
0.19249 1
 
0.1%
0.23442 1
 
0.1%
0.24749 1
 
0.1%
ValueCountFrequency (%)
1.644 1
0.1%
1.624 1
0.1%
1.610574007 1
0.1%
1.601 1
0.1%
1.592 1
0.1%
1.59 1
0.1%
1.587 1
0.1%
1.584 1
0.1%
1.583 1
0.1%
1.582 2
0.3%

health
Real number (ℝ)

HIGH CORRELATION 

Distinct705
Distinct (%)90.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.61241558
Minimum0
Maximum1.141
Zeros5
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2024-07-30T21:10:12.721802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1578945
Q10.4401825
median0.64730951
Q30.808
95-th percentile0.954973
Maximum1.141
Range1.141
Interquartile range (IQR)0.3678175

Descriptive statistics

Standard deviation0.24830864
Coefficient of variation (CV)0.40545775
Kurtosis-0.48757121
Mean0.61241558
Median Absolute Deviation (MAD)0.16864469
Skewness-0.50120256
Sum478.90898
Variance0.061657181
MonotonicityNot monotonic
2024-07-30T21:10:12.951225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5
 
0.6%
0.999 5
 
0.6%
0.815 5
 
0.6%
0.828 4
 
0.5%
0.874 4
 
0.5%
0.884 3
 
0.4%
0.914 3
 
0.4%
0.854 3
 
0.4%
0.871 3
 
0.4%
0.861 3
 
0.4%
Other values (695) 744
95.1%
ValueCountFrequency (%)
0 5
0.6%
0.005564753897 1
 
0.1%
0.01 1
 
0.1%
0.0187726859 1
 
0.1%
0.03824 1
 
0.1%
0.04113471508 1
 
0.1%
0.04476 1
 
0.1%
0.04776 1
 
0.1%
0.048 1
 
0.1%
0.04864216968 1
 
0.1%
ValueCountFrequency (%)
1.141 1
0.1%
1.122 1
0.1%
1.088 1
0.1%
1.062 1
0.1%
1.052 1
0.1%
1.045 1
0.1%
1.042 2
0.3%
1.039 2
0.3%
1.036 2
0.3%
1.03 1
0.1%

freedom
Real number (ℝ)

HIGH CORRELATION 

Distinct697
Distinct (%)89.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.41109083
Minimum0
Maximum0.724
Zeros5
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2024-07-30T21:10:13.174559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.128096
Q10.3097675
median0.431
Q30.531
95-th percentile0.6308885
Maximum0.724
Range0.724
Interquartile range (IQR)0.2212325

Descriptive statistics

Standard deviation0.15288042
Coefficient of variation (CV)0.37188964
Kurtosis-0.30720541
Mean0.41109083
Median Absolute Deviation (MAD)0.10948
Skewness-0.52125913
Sum321.47303
Variance0.023372423
MonotonicityNot monotonic
2024-07-30T21:10:13.421007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5
 
0.6%
0.557 4
 
0.5%
0.417 3
 
0.4%
0.312 3
 
0.4%
0.356 3
 
0.4%
0.516 3
 
0.4%
0.406 3
 
0.4%
0.531 3
 
0.4%
0.508 3
 
0.4%
0.431 3
 
0.4%
Other values (687) 749
95.8%
ValueCountFrequency (%)
0 5
0.6%
0.00589 1
 
0.1%
0.01 1
 
0.1%
0.013 1
 
0.1%
0.01499585528 1
 
0.1%
0.016 1
 
0.1%
0.025 1
 
0.1%
0.026 1
 
0.1%
0.03036985733 1
 
0.1%
0.0432 1
 
0.1%
ValueCountFrequency (%)
0.724 1
0.1%
0.696 1
0.1%
0.686 1
0.1%
0.683 1
0.1%
0.681 1
0.1%
0.677 1
0.1%
0.674 1
0.1%
0.66973 1
0.1%
0.669 1
0.1%
0.66557 1
0.1%

trust
Real number (ℝ)

Distinct635
Distinct (%)81.3%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.12543561
Minimum0
Maximum0.55191
Zeros6
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2024-07-30T21:10:13.653898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.018
Q10.054
median0.091
Q30.15603
95-th percentile0.37124
Maximum0.55191
Range0.55191
Interquartile range (IQR)0.10203

Descriptive statistics

Standard deviation0.10581645
Coefficient of variation (CV)0.84359174
Kurtosis1.8801083
Mean0.12543561
Median Absolute Deviation (MAD)0.04745
Skewness1.5208882
Sum97.965214
Variance0.011197121
MonotonicityNot monotonic
2024-07-30T21:10:13.878913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.082 7
 
0.9%
0.028 6
 
0.8%
0.064 6
 
0.8%
0.078 6
 
0.8%
0 6
 
0.8%
0.034 6
 
0.8%
0.056 5
 
0.6%
0.074 5
 
0.6%
0.055 5
 
0.6%
0.093 5
 
0.6%
Other values (625) 724
92.6%
ValueCountFrequency (%)
0 6
0.8%
0.001 1
 
0.1%
0.00227 1
 
0.1%
0.00322 1
 
0.1%
0.004 1
 
0.1%
0.004387900699 1
 
0.1%
0.005 1
 
0.1%
0.006 3
0.4%
0.00615 1
 
0.1%
0.00649 1
 
0.1%
ValueCountFrequency (%)
0.55191 1
0.1%
0.52208 1
0.1%
0.50521 1
0.1%
0.4921 1
0.1%
0.48357 1
0.1%
0.48049 1
0.1%
0.46987 1
0.1%
0.464307785 1
0.1%
0.457 1
0.1%
0.4552200139 1
0.1%

generosity
Real number (ℝ)

Distinct664
Distinct (%)84.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.21857584
Minimum0
Maximum0.83807516
Zeros5
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2024-07-30T21:10:14.109955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.054030375
Q10.13
median0.20198221
Q30.2788325
95-th percentile0.47045434
Maximum0.83807516
Range0.83807516
Interquartile range (IQR)0.1488325

Descriptive statistics

Standard deviation0.12232075
Coefficient of variation (CV)0.55962611
Kurtosis2.0202583
Mean0.21857584
Median Absolute Deviation (MAD)0.07322149
Skewness1.04436
Sum170.92631
Variance0.014962366
MonotonicityNot monotonic
2024-07-30T21:10:14.576006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.175 6
 
0.8%
0.153 5
 
0.6%
0.187 5
 
0.6%
0 5
 
0.6%
0.099 4
 
0.5%
0.083 4
 
0.5%
0.197 4
 
0.5%
0.142 3
 
0.4%
0.148 3
 
0.4%
0.185 3
 
0.4%
Other values (654) 740
94.6%
ValueCountFrequency (%)
0 5
0.6%
0.00199 1
 
0.1%
0.01016465668 1
 
0.1%
0.02025 1
 
0.1%
0.025 1
 
0.1%
0.026 2
 
0.3%
0.02641 1
 
0.1%
0.02736 1
 
0.1%
0.028806841 1
 
0.1%
0.029 1
 
0.1%
ValueCountFrequency (%)
0.838075161 1
0.1%
0.81971 1
0.1%
0.79588 1
0.1%
0.6117045879 1
0.1%
0.598 1
0.1%
0.58696 1
0.1%
0.5763 1
0.1%
0.5747305751 1
0.1%
0.5721231103 1
0.1%
0.566 1
0.1%

dystopia
Real number (ℝ)

MISSING 

Distinct470
Distinct (%)100.0%
Missing312
Missing (%)39.9%
Infinite0
Infinite (%)0.0%
Mean2.0927166
Minimum0.32858
Maximum3.83772
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2024-07-30T21:10:14.794167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.32858
5-th percentile1.1264927
Q11.737975
median2.09464
Q32.4555745
95-th percentile3.025559
Maximum3.83772
Range3.50914
Interquartile range (IQR)0.71759955

Descriptive statistics

Standard deviation0.56577176
Coefficient of variation (CV)0.27035278
Kurtosis0.41413063
Mean2.0927166
Median Absolute Deviation (MAD)0.35951323
Skewness-0.12164695
Sum983.57682
Variance0.32009768
MonotonicityNot monotonic
2024-07-30T21:10:15.031125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.9521 1
 
0.1%
1.59888 1
 
0.1%
1.878890276 1
 
0.1%
2.40364 1
 
0.1%
2.04384 1
 
0.1%
2.79248929 1
 
0.1%
3.18286 1
 
0.1%
3.10709 1
 
0.1%
2.47489 1
 
0.1%
2.277026653 1
 
0.1%
Other values (460) 460
58.8%
(Missing) 312
39.9%
ValueCountFrequency (%)
0.32858 1
0.1%
0.3779137135 1
0.1%
0.4193892479 1
0.1%
0.5400612354 1
0.1%
0.5546331406 1
0.1%
0.6211304665 1
0.1%
0.65429 1
0.1%
0.67042 1
0.1%
0.67108 1
0.1%
0.8143823147 1
0.1%
ValueCountFrequency (%)
3.83772 1
0.1%
3.60214 1
0.1%
3.55906 1
0.1%
3.50733 1
0.1%
3.40904 1
0.1%
3.38007 1
0.1%
3.35168 1
0.1%
3.31029 1
0.1%
3.26001 1
0.1%
3.22134 1
0.1%

Interactions

2024-07-30T21:10:06.561307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:09:50.236985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:09:52.649205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:09:54.356684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:09:56.415021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:09:58.064630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:09:59.761885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:10:01.564119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:10:03.272068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:10:04.936043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:10:06.738314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:09:50.476525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:09:52.836206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:09:54.535866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:09:56.601515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:09:58.250767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:09:59.943908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:10:01.749047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:10:03.451187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:10:05.118202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:10:06.916460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:09:51.118119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:09:53.013211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:09:54.707346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:09:56.778673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:09:58.433901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:10:00.123898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:10:01.932945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:10:03.628081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:10:05.290440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:10:07.250372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:09:51.291373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:09:53.176884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:09:54.861055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:09:56.937663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:09:58.601368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:10:00.280816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:10:02.098947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:10:03.793366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:10:05.448219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:10:07.416367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:09:51.542969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:09:53.336274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:09:55.015504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:09:57.089656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:09:58.757677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:10:00.436835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:10:02.257959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:10:03.964240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:10:05.599230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:10:07.590410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:09:51.793972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:09:53.506246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:09:55.342071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:09:57.252561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:09:58.924500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:10:00.603620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:10:02.430926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:10:04.131948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:10:05.765245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:10:07.743409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:09:51.964967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:09:53.673610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:09:55.580737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:09:57.408557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:09:59.092730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:10:00.760776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:10:02.599496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:10:04.293119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:10:05.926543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:10:07.911474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:09:52.147964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:09:53.857336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:09:55.785579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:09:57.577582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:09:59.269718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:10:01.089247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:10:02.776029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:10:04.464154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:10:06.094276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:10:08.071439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:09:52.315198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:09:54.027822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:09:55.951454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:09:57.747600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:09:59.432870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:10:01.249011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:10:02.941010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:10:04.622154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:10:06.252295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:10:08.223451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:09:52.480300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:09:54.189371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:09:56.168481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:09:57.899940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:09:59.592759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:10:01.402932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:10:03.103023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:10:04.775233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-30T21:10:06.401317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-07-30T21:10:15.203573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
dystopiafamilyfreedomgdp_pcgenerosityhealthrankregionscoretrustyear
dystopia1.000-0.0770.0490.071-0.0290.102-0.4900.1490.4980.066-0.191
family-0.0771.0000.4350.584-0.0400.586-0.6470.2060.6450.0500.398
freedom0.0490.4351.0000.3630.3320.361-0.5420.2120.5420.4270.017
gdp_pc0.0710.5840.3631.0000.0000.795-0.8080.3440.8060.2200.017
generosity-0.029-0.0400.3320.0001.0000.009-0.1210.2470.1220.272-0.183
health0.1020.5860.3610.7950.0091.000-0.7580.3730.7620.1500.128
rank-0.490-0.647-0.542-0.808-0.121-0.7581.0000.347-0.999-0.273-0.008
region0.1490.2060.2120.3440.2470.3730.3471.0000.3570.2550.000
score0.4980.6450.5420.8060.1220.762-0.9990.3571.0000.2730.012
trust0.0660.0500.4270.2200.2720.150-0.2730.2550.2731.000-0.121
year-0.1910.3980.0170.017-0.1830.128-0.0080.0000.012-0.1211.000

Missing values

2024-07-30T21:10:08.459466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-30T21:10:08.749476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-07-30T21:10:08.944732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

country_mappedregionyearrankscoregdp_pcfamilyhealthfreedomtrustgenerositydystopia
0AfghanistanSouthern Asia20151533.5750.3198200.3028500.3033500.2341400.0971900.3651001.952100
1AfghanistanSouthern Asia20161543.3600.3822700.1103700.1734400.1643000.0711200.3126802.145580
2AfghanistanSouthern Asia20171413.7940.4014770.5815430.1807470.1061800.0611580.3118712.150801
3AfghanistanSouthern Asia20181453.6320.3320000.5370000.2550000.0850000.0360000.191000NaN
4AfghanistanSouthern Asia20191543.2030.3500000.5170000.3610000.0000000.0250000.158000NaN
5AlbaniaCentral and Eastern Europe2015954.9590.8786700.8043400.8132500.3573300.0641300.1427201.898940
6AlbaniaCentral and Eastern Europe20161094.6550.9553000.5016300.7300700.3186600.0530100.1684001.928160
7AlbaniaCentral and Eastern Europe20171094.6440.9961930.8036850.7311600.3814990.0398640.2013131.490442
8AlbaniaCentral and Eastern Europe20181124.5860.9160000.8170000.7900000.4190000.0320000.149000NaN
9AlbaniaCentral and Eastern Europe20191074.7190.9470000.8480000.8740000.3830000.0270000.178000NaN
country_mappedregionyearrankscoregdp_pcfamilyhealthfreedomtrustgenerositydystopia
772ZambiaSub-Saharan Africa2015855.1290.4703800.9161200.2992400.4882700.1246800.1959102.634300
773ZambiaSub-Saharan Africa20161064.7950.6120200.6376000.2357300.4266200.1147900.1786602.589910
774ZambiaSub-Saharan Africa20171164.5140.6364071.0031870.2578360.4616030.0782140.2495801.826705
775ZambiaSub-Saharan Africa20181254.3770.5620001.0470000.2950000.5030000.0820000.221000NaN
776ZambiaSub-Saharan Africa20191384.1070.5780001.0580000.4260000.4310000.0870000.247000NaN
777ZimbabweSub-Saharan Africa20151154.6100.2710001.0327600.3347500.2586100.0807900.1898702.441910
778ZimbabweSub-Saharan Africa20161314.1930.3504100.7147800.1595000.2542900.0858200.1850302.442700
779ZimbabweSub-Saharan Africa20171383.8750.3758471.0830960.1967640.3363840.0953750.1891431.597970
780ZimbabweSub-Saharan Africa20181443.6920.3570001.0940000.2480000.4060000.0990000.132000NaN
781ZimbabweSub-Saharan Africa20191463.6630.3660001.1140000.4330000.3610000.0890000.151000NaN